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Orthodb: the Hierarchical Catalog of Eukaryotic Orthologs Evgenia V

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Orthodb: the Hierarchical Catalog of Eukaryotic Orthologs Evgenia V Published online 18 October 2007 Nucleic Acids Research, 2008, Vol. 36, Database issue D271–D275 doi:10.1093/nar/gkm845 OrthoDB: the hierarchical catalog of eukaryotic orthologs Evgenia V. Kriventseva3, Nazim Rahman1, Octavio Espinosa1 and Evgeny M. Zdobnov1,2,4,* 1Department of Genetic Medicine and Development, University of Geneva Medical School, 2Swiss Institute of Bioinformatics, 1 rue Michel-Servet, 3Department of Structural Biology and Bioinformatics, University of Geneva Medical School, 1 rue Michel-Servet, 1211 Geneva, Switzerland and 4Imperial College London, South Kensington Campus, SW7 2AZ London, UK Received August 20, 2007; Revised September 24, 2007; Accepted September 25, 2007 ABSTRACT particularly challenging with complex eukaryotic genomes. The fast growing number of available complete The concept of orthology is widely used to relate genomes facilitates a much better resolution of the gene genes across different species using comparative genealogies, while at the same time greatly increasing the genomics, and it provides the basis for inferring computational challenges. gene function. Here we present the web accessible There are two main approaches to delineate ortholo- OrthoDB database that catalogs groups of ortho- gous genes: (i) from reconciliation of gene trees with logous genes in a hierarchical manner, at each the species phylogeny and (ii) from classification of all- radiation of the species phylogeny, from more against-all sequence comparisons of complete genomes. general groups to more fine-grained delineations The phylogeny approach takes advantage of well-studied between closely related species. We used a evolution of conserved cores of globular proteins using COG-like and Inparanoid-like ortholog delineation quantitative models of amino acid substitutions (4–6). The notable examples of the tree-based approach to procedure on the basis of all-against-all Smith- delineate orthologous genes are HOVERGEN (7) and Waterman sequence comparisons to analyze TreeFam (8). The expert curation of the phylogenetic trees 58 eukaryotic genomes, focusing on vertebrates, and the underlying multiple sequence alignments is both, insects and fungi to facilitate further compara- advantageous, providing better accuracy and disadvanta- tive studies. The database is freely available at geous, limiting the comprehensiveness, homogeneity of http://cegg.unige.ch/orthodb quality and expandability to new species. Although given the appropriate data phylogenetic methods are likely to give more accurate models of ancestral sequences and INTRODUCTION therefore to yield more accurate orthology prediction, their applicability to current genomic data is hindered Identification of orthologous genes is the cornerstone of by several factors, most importantly: (i) they require sub- comparative genomics, which is increasingly becoming stantially more computational resources, (ii) the reconci- an essential part of modern molecular biology. liation of gene and species trees relies on poorly quantified Functions of orthologous genes are often preserved models of gene duplication and loss, and (iii) they are through evolution, as by definition, orthologous genes sensitive to completeness of predicted genes as the descend by speciation from the common ancestor gene evolutionary models are designed for only well-conserved (1–3). Although the conservation of ortholog functions globular cores of proteins and missing data (gaps) render is not required or guaranteed, it is the most likely the approach inapplicable. The tree-based approaches also evolutionary scenario and provides a strong working require the knowledge of the species phylogeny, and hypothesis, particularly when the ortholog copy-number although the consensus on animal phylogeny seems to be is preserved over a long period of time. Identification close, it is still constantly challenged. The alternative of orthologs is intricate as it assumes knowledge of approach of clustering orthologous genes on the basis of ancestral state of the genes, and it requires knowledge their whole-length similarity around Best-Reciprocal-Hits of the complete gene repertoires. It is also complicated (BRHs, also known as SymBets, bi-directional BeTs by gene duplication, fusion and exon shuffling, as well and best–best hits, denoting sequences most similar to as pseudonization and loss, which make the problem each other in between-genome comparisons) was first *To whom correspondence should be addressed. Tel: +41 22 379 59 73; Fax: +41 22 379 57 06; Email: [email protected] ß 2007 The Author(s) This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. D272 Nucleic Acids Research, 2008, Vol. 36, Database issue introduced by the database of Clusters of Orthologous METHODS Groups (COGs) (9). Triggered by the earlier availability Orthology delineation of much smaller and simpler bacterial genomes the database has quickly gained wide recognition and was Groups of orthologous genes were automatically identi- later extrapolated to eukaryotic genomes (KOGs) (9). fied using a strategy employed previously (19–22) that is The identification of BRHs is widely adopted currently based on all-against-all protein sequence comparisons in the field of comparative genomics for its simplicity and using the Smith-Waterman algorithm as implemented feasibility of application to large-scale data. In terms of in ParAlign (23) with default parameters, followed by phylogenetic trees, BRHs could be interpreted as genes clustering of best reciprocal hits from highest scoring ones À6 À10 from different species with the shortest connecting path to 10 e-value cutoff for triangulating BRH or 10 over the distance-based tree. The simplest application of cutoff for unsupported BRH, and requiring a sequence this approach using BLAST (10) for interspecies compar- alignment overlap of at least 30 amino acids across all isons suffers from inaccuracies of sequence distance members of a group. Furthermore, the orthologous estimates and ignores many gene duplications after the groups were expanded by genes that are more similar to speciation that are, in fact, co-orthologs that are difficult each other within a proteome than to any gene in any of to differentiate functionally. However, using these genes the other species, and by very similar copies that share as anchors of orthologous groups in different species, over 97% sequence identity, which were identified initially additional co-orthologs can be identified as genes that using CD-Hit (24). We considered only the longest trans- are more similar to them in intra-genome comparisons cript per gene or the most common as specified in UniProt than to any other gene in the other genomes, as (25). The outlined procedure was first applied to all species popularized by the pairwise Inparanoid approach (11). considered, and then to each subset of species according to There are also a few alternative clustering heuristics with the radiation of the phylogenetic tree. varying compromises between specificity and selectivity (12) that focus on the growing number of available Phylogeny reconstruction eukaryotic genomes such as the probabilistic approach of To guide computation of the ortholog hierarchy we OrthoMCL (13) and the vertebrate-centric Ensembl- produced the multiple alignment of concatenated single- Compara (14). copy orthologs, using well-aligned regions extracted with Another important feature of orthology and paralogy Gblocks (26) from individually aligned orthologous classification, which is currently underappreciated, is that sequences using Muscle (27). This was used to compute it is relative to a particular ancestor, as orthology of genes the phylogenetic trees using the maximum-likelihood is defined by their descent from a common ancestor gene method as implemented in PHYML (28), employing the by speciation (1–3). Therefore, the more distantly related JTT model, a gamma correction with four discrete classes, species are considered the more general (inclusive) and an estimated alpha parameter and proportion of orthologous groups become, because all lineage-specific invariable sites. duplications since this last common ancestor should be considered as co-orthologs. Inversely, orthologous groups become more fine-grained (more 1 : 1 relations) when DATABASE CONTENT closely related species are considered, as there was less Overview statistics time for gene duplications to occur. The concept of hierarchical orthologous groups has already prompted As detailed in Table 1 we analyzed 23 complete proteomes of fungal species, 19 insects and 15 vertebrates at different development of Levels of Orthology From Trees (LOFT) levels of the species phylogeny. Overall, this effort spans (15) tool to interpret the gene-trees in the context of 870 737 genes, 82% of which have been classified into species tree, COrrelation COefficient-based CLustering 10 876 orthologous groups in fungi, 19 835 in insects and (COCO-CL) (16) methodology to refine clusters of 23 940 in vertebrates, providing the first systematic homologous genes, and PHOG approach (17) to resolve classification of the wealth of data that will provide the orthology at each taxonomy node using explicit modeling basis for further comparative evolutionary analyses. of the ancestral sequences and relying on PHOG-BLAST
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